Relationship between cognitive impairment and hypercholesterolemia in elderly patients with white matter lesions
Bibliographic record
Abstract
Objective To study the relationship between cognitive impairment and hypercholesterolemia as well as the risk factors thereof in elderly patients with white matter lesions (WML). Methods A total of 347 WML patients were divided into normal cognitive group (n=86) and cognitive impairment group (n=261) according to their mini-mental state examination (MMSE) score. The general situation of the two groups was compared and the correlation of hypercholesterolemia with WML severity and cognitive function was analyzed; the risk factors of cognitive impairment in WML patients were analyzed by logistic regression analysis. Results Compared with those in normal cognitive group, the patients in cognitive impairment group were older, the plasma low-density lipoprotein (LDL) level and the proportion of male, low education level, hypertension and hypercholesterolemia were significantly higher (P<0.05), while MMSE score and the proportion of patients with a history of statins were significantly lower (P<0.05). The proportion of patients with cognitive impairment significantly increased (P=0.001), and the incidence of hypercholesterolemia also increased significantly (P=0.000) with the increasing of WML severity. The Montreal Congnitive Assessment (MoCA) score, visuospatial and executive function, attention and computing power, language and abstract ability, and delayed recall scores of patients with hypercholesterolemia were significantly lower than that of patients without hypercholesterolemia (P<0.05). Logistic regression analysis showed that low education level, hypertension, hypercholesterolemia and the higher level of plasma LDL were independent risk factors of cognitive impairment in patients with WML (P<0.05), while the history of statins use was a protective factor (P<0.05). Conclusion Hypercholesterolemia can significantly increase the risk of cognitive impairment in patients with WML, thus the WML patients with hypercholesterolemia should be focused, and various risk factors should be controlled, at the same time the fortified early intervention is necessary.\n\t\t\n\t\tDOI: 10.11855/j.issn.0577-7402.2016.12.08
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".